JP2013109550A - Home energy consumption prediction system, home energy consumption prediction server, home energy consumption prediction method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately predict an amount of electric power demand for each consumer, and to exhibit a value of the amount of electric power demand.SOLUTION: An energy consumption prediction server comprises a database for associating and storing past energy consumption information received from an energy consumption measuring device and an environmental information collection device. The energy consumption prediction server predicts home energy consumption from information received from an environmental information collection device and the information in the database, and displays predicted results given by the energy consumption prediction server.

Description

  The present invention relates to energy consumption prediction, and more particularly, to a home energy consumption prediction system, a home energy consumption prediction server, a home energy consumption prediction method, and a program that perform home energy consumption prediction.

  Due to the impact of the recent earthquake disaster, products and technology development on the themes of energy saving and power saving are exciting in each company. In addition, there is a concern that sudden unplanned power outages may occur when the amount of power demand exceeds the amount of supply in summer and winter due to problems such as the operation of the nuclear power plant. For this reason, not only is it a company that becomes a major customer within the jurisdiction of electric power companies, but it is not mandatory, but each home also has a 15% increase from the previous year between 9am and 8pm on weekdays from July to September. (Comparative) reduction effort target.

  Conventionally, as technology related to power demand forecasting, power demand forecasting within the power company's jurisdiction is based on past temperature data, power demand data is predicted based on power demand data, and forecast values are corrected based on past weather change patterns. Etc. are known (for example, refer to Patent Document 1).

Japanese Patent Laid-Open No. 5-38051

  However, as typified by the above-mentioned conventional technology, in the past, most of the technology related to the system or mechanism related to prediction within the jurisdiction of electric power companies occupies the majority, and active consideration has not been made regarding prediction in the home. Currently. As before, when the power supply of the power company is sufficient, the prediction within the power company's jurisdiction is sufficient, but as in the current situation, the power supply of the power company has been greatly reduced. Further, it is necessary to predict the power demand for each household, display it, and promote the power saving action by the consumer recognizing it.

  Therefore, the present invention has been made in view of the above-described problems, and accurately predicts a power demand amount for each consumer and presents the value, a home energy consumption prediction server. An object of the present invention is to provide a method and program for predicting residential energy consumption.

  The present invention proposes the following items in order to solve the above-described problems. In addition, in order to make an understanding easy, although the code | symbol corresponding to embodiment of this invention is attached | subjected and demonstrated, it is not limited to this.

  (1) The present invention provides an energy consumption prediction server (for example, equivalent to the energy consumption prediction server 200 of FIG. 1) for predicting the energy consumption in the user's home, via the energy consumption prediction server and the network. Connected energy consumption measuring device (for example, equivalent to the energy consumption measuring device 110 of FIG. 1), environmental information acquisition device for acquiring environmental information outside the house (for example, equivalent to the environmental information acquiring device 120 of FIG. 1) A display device (for example, corresponding to the display device 310 of FIG. 1) that displays a prediction result of the energy consumption prediction server, and the energy consumption prediction server collects the energy consumption measurement device and environmental information. A database (for example, the database in FIG. 2) that stores past energy consumption information received from the apparatus in association with each other. 212) and energy consumption prediction means for predicting energy consumption in the home from information received from the environmental information collection device and information in the database (for example, equivalent to the predicted value calculation unit 214 in FIG. 2) And the display device displays a prediction result of the energy consumption prediction server, and proposes a home energy consumption prediction system.

  According to this invention, the energy consumption prediction server includes a database that stores past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other, and the energy consumption prediction means includes the environment The energy consumption amount in the house is predicted from the information received from the information collection device and the information in the database, and the display device displays the prediction result of the energy consumption amount prediction server. Therefore, for example, to predict the energy consumption from the current or the next day from the acquired data of the past several years, and display the prediction result, the user can be notified of the energy consumption from the current or the next day, Users can be encouraged to save electricity.

  (2) In the home energy consumption prediction system according to (1), the database includes at least a weekday database that stores weekday data and a holiday database that stores holiday data. The consumption prediction server has a calendar function, and also includes selection means (for example, equivalent to the DB type selection unit 213 in FIG. 2) that selects the type of the database used for energy consumption prediction based on the calendar information. A home energy consumption forecasting system is proposed.

  According to the present invention, the database includes at least a weekday database storing weekday data and a holiday database storing holiday data, the energy consumption prediction server includes a calendar function, and the selection means includes Based on the calendar information, the type of database used for energy consumption prediction is selected. In other words, because it is assumed that lifestyles differ greatly on weekdays and holidays (holidays and weekends), the database is divided into two types, “weekday type” and “holiday type”, and either database is selected according to the calendar. By this, the energy consumption from the present or the following day can be predicted more accurately.

  (3) The present invention includes a location information acquisition device that acquires at least the location information of the user in the home energy consumption prediction system of (1), and the database stores data when the user is in the home. And the energy consumption prediction server is based on the location information acquired by the location information acquisition device, and the user uses the location information acquisition device to store the data when the user is outside the home. Characterized by comprising selection means (for example, equivalent to the DB type selection unit 223 in FIG. 9) for selecting the type of the database depending on whether the user is in the house or outside the house An energy consumption prediction system is proposed.

  According to this invention, the database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home, and the energy consumption amount The position information acquisition device of the prediction server acquires at least the position information of the user, and the selection unit of the energy consumption prediction server determines whether the user is in the house based on the position information acquired by the position information acquisition device, or The database type is selected according to whether the user is away from home. In other words, two types of predicted values at home and when going out are prepared by grasping the user's home status by linking with location information, and the current or next day's energy consumption is predicted using this. By doing so, the prediction accuracy of energy consumption can be further improved.

  (4) The present invention proposes a home energy consumption prediction system in which the position information acquisition device is a GPS in a mobile terminal in the home energy consumption prediction system of (3).

  According to this invention, the position information acquisition device is a GPS in the mobile terminal. That is, the user's location information can be easily acquired by using the GPS installed in many portable terminals.

  (5) The present invention relates to a home energy consumption prediction system according to (1), which manages at least the user's schedule information and transmits the schedule information to the energy consumption prediction server (for example, 11 corresponding to the schedule information management unit 231 in FIG. 11, and the database is an in-home type database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home. And when the energy consumption prediction server receives the schedule information, it selects a database type according to the received schedule information (e.g., equivalent to the DB type selection unit 233 of FIG. 11). Proposal of a residential energy consumption forecasting system characterized by To have.

  According to the present invention, the schedule information distribution management device manages at least user schedule information and transmits the schedule information to the energy consumption prediction server. The database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home, and the energy consumption prediction server receives the schedule information. The selecting means selects the type of database according to the received schedule information. That is, by cooperating with the schedule information, it is possible to perform a prediction calculation that distinguishes whether the user is in the house or outside the user, and can further improve the energy consumption prediction accuracy.

  (6) The present invention relates to the in-home energy consumption prediction system according to (1) to (5), wherein the in-house database corresponds to the combination of the housemates in the house among the housemates in the user house, We propose a home energy consumption forecasting system that is subdivided into a database that stores data.

  According to the present invention, the in-house database is subdivided into a database in which data is stored corresponding to the combination of the housemates in the house among the housemates in the user's house. That is, since the home database is subdivided corresponding to the family structure in the home, the energy consumption prediction accuracy can be further improved.

  (7) The present invention proposes a home energy consumption prediction system, wherein the energy consumption prediction server acquires an unpleasantness index for the home energy consumption prediction system of (1) to (6). ing.

  According to the present invention, the energy consumption prediction server acquires the discomfort index. That is, it is assumed that an air conditioner such as an air conditioner that takes up most of the power consumption is greatly influenced not only by the temperature but also by the humidity. Therefore, in consideration of these factors, it is possible to predict the energy consumption using the discomfort index as an index and improve the accuracy of the predicted value.

  (8) The present invention is connected to an energy consumption measuring device that measures energy consumption in a home, an environment information acquisition device that obtains environmental information outside the home, and a display device via a network, and at least home energy. A home energy consumption prediction server that displays a consumption prediction result, a database that stores past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other, and the environment information collection There is proposed a home energy consumption prediction server comprising: energy consumption prediction means for predicting home energy consumption from information received from a device and information in the database.

  According to this invention, there is provided a database that stores past energy consumption information received from the energy consumption measuring device and the environment information collecting device in association with each other, and the information that the energy consumption prediction means receives from the environment information collecting device. And the energy consumption in the home is predicted from the information in the database. Therefore, for example, in order to predict the energy consumption from the current or the next day from the acquired data for the past several years, the user can be notified of the energy consumption from the current or the next day, thereby encouraging the user to save electricity. be able to.

  (9) The present invention relates to the residential energy consumption prediction server of (8), wherein the database includes at least a weekday database storing weekday data and a holiday database storing holiday data, and a calendar function And a home energy consumption prediction server characterized by comprising selection means for selecting the type of the database used for energy consumption prediction based on the calendar information.

  According to the present invention, the database includes at least a weekday database storing weekday data and a holiday database storing holiday data, the energy consumption prediction server includes a calendar function, and the selection means includes Based on the calendar information, the type of database used for energy consumption prediction is selected. In other words, because it is assumed that lifestyles differ greatly on weekdays and holidays (holidays and weekends), the database is divided into two types, “weekday type” and “holiday type”, and either database is selected according to the calendar. By this, the energy consumption from the present or the following day can be predicted more accurately.

  (10) The present invention relates to the in-home energy consumption prediction server of (8), wherein the database stores an in-home type database storing data when the user is in the home and data when the user is outside the home. According to whether the user is in the house or out of the house, based on at least the position information acquired by the position information acquisition apparatus that acquires the position information of the user. In addition, a home energy consumption prediction server is provided that includes selection means for selecting the type of the database.

  According to this invention, the database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home, and the energy consumption amount The position information acquisition device of the prediction server acquires at least the position information of the user, and the selection unit of the energy consumption prediction server determines whether the user is in the house based on the position information acquired by the position information acquisition device, or The database type is selected according to whether the user is away from home. In other words, two types of predicted values at home and when going out are prepared by grasping the user's home status by linking with location information, and the current or next day's energy consumption is predicted using this. By doing so, the prediction accuracy of energy consumption can be further improved.

  (11) The present invention proposes a home energy consumption prediction server characterized in that the location information acquisition device is a GPS in a mobile terminal with respect to the home energy consumption prediction server of (10).

  According to this invention, the position information acquisition device is a GPS in the mobile terminal. That is, the user's location information can be easily acquired by using the GPS installed in many portable terminals.

  (12) The present invention includes a schedule information distribution management device that manages at least the user's schedule information and transmits the schedule information to the energy consumption prediction server for the in-home energy consumption prediction server of (8), The database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home, and when the schedule information is received, the received database A home energy consumption prediction server is proposed that includes selection means for selecting the type of database according to schedule information.

  According to the present invention, the schedule information distribution management device manages at least user schedule information and transmits the schedule information to the energy consumption prediction server. The database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home, and the energy consumption prediction server receives the schedule information. The selecting means selects the type of database according to the received schedule information. That is, by cooperating with the schedule information, it is possible to perform a prediction calculation that distinguishes whether the user is in the house or outside the user, and can further improve the energy consumption prediction accuracy.

  (13) The present invention relates to the in-house energy consumption prediction server of (8) to (12), wherein the in-house database corresponds to a combination of the housemates in the house among the housemates in the user house, We have proposed a home energy consumption prediction server characterized by being subdivided into a database that stores data.

  According to the present invention, the in-house database is subdivided into a database in which data is stored corresponding to the combination of the housemates in the house among the housemates in the user's house. That is, since the home database is subdivided corresponding to the family structure in the home, the energy consumption prediction accuracy can be further improved.

  (14) The present invention proposes a home energy consumption prediction system characterized by acquiring a discomfort index for the home energy consumption prediction servers of (8) to (13).

  According to this invention, the residential energy consumption prediction server acquires the discomfort index. That is, it is assumed that an air conditioner such as an air conditioner that takes up most of the power consumption is greatly influenced not only by the temperature but also by the humidity. Therefore, in consideration of these factors, it is possible to predict the energy consumption using the discomfort index as an index and improve the accuracy of the predicted value.

  (15) The present invention relates to an energy consumption prediction server that predicts energy consumption in a user's home, an energy consumption measurement device connected to the energy consumption prediction server via a network, and environmental information outside the home. A home energy consumption prediction method in a home energy consumption prediction system comprising an environmental information acquisition device to be acquired and at least a display device for displaying a prediction result of the energy consumption prediction server, wherein the energy consumption prediction server comprises: A database that stores past energy consumption information received from the energy consumption measuring device and the environment information collecting device in association with each other, and the information that the energy consumption prediction server receives from the environment information collecting device; Predicting energy consumption in the home from information in the database It has proposed an inner energy consumption prediction method.

  According to the present invention, the energy consumption prediction server includes the database that stores the past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other, and receives information from the environment information collection device The energy consumption amount in the home is predicted from the information in the database and the prediction result of the energy consumption prediction server is displayed. Therefore, it predicts the energy consumption from the past or the next day from the past data and displays the prediction result, so the user can be notified of the energy consumption from the current or the next day. Can be urged.

  (16) The present invention relates to an energy consumption prediction server that predicts energy consumption in a user's home, an energy consumption measurement device connected to the energy consumption prediction server via a network, and environmental information outside the house. A program for causing a computer to execute a home energy consumption prediction method in a home energy consumption prediction system including an environmental information acquisition device to be acquired, and a display device that displays at least a prediction result of the energy consumption prediction server, The energy consumption prediction server includes a database that stores past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other, and the energy consumption prediction server includes the environment information. Information received from the collection device and in the database It has proposed a program to predict the energy consumption of the home from the broadcast.

  According to the present invention, the energy consumption prediction server includes the database that stores the past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other, and receives information from the environment information collection device The energy consumption amount in the home is predicted from the information in the database and the prediction result of the energy consumption prediction server is displayed. Therefore, it predicts the energy consumption from the past or the next day from the past data and displays the prediction result, so the user can be notified of the energy consumption from the current or the next day. Can be urged.

  According to the present invention, for example, since the user can grasp the power consumption trend of the next day, power saving behavior can be performed such as reluctance to use an electric device used during a time zone when power consumption is large or going out. It becomes like this. As a result, in particular, the power saving effect during the peak hours of power demand within the jurisdiction of the power company, for example, from 9:00 to 20:00 on weekdays is increased, and the occurrence of large-scale power outages can be prevented.

It is a figure which shows the structure of the residential energy consumption prediction system which concerns on this invention. It is a figure which shows the structure of the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which illustrates the database in the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which illustrates the database in the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which illustrates the database in the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which illustrates the database in the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which shows the relationship between the discomfort index | exponent based on the 1st Embodiment of this invention, and bodily sensation. It is a figure which shows the process of the energy consumption prediction server which concerns on the 1st Embodiment of this invention. It is a figure which shows the structure of the energy consumption prediction server which concerns on the 2nd Embodiment of this invention. It is a figure which shows the process of the energy consumption prediction server which concerns on the 2nd Embodiment of this invention. It is a figure which shows the structure of the energy consumption prediction server which concerns on the 3rd Embodiment of this invention. It is a figure which shows the process of the energy consumption prediction server which concerns on the 3rd Embodiment of this invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
Note that the constituent elements in the present embodiment can be appropriately replaced with existing constituent elements and the like, and various variations including combinations with other existing constituent elements are possible. Therefore, the description of the present embodiment does not limit the contents of the invention described in the claims.

<Configuration of home energy consumption forecasting system>
The configuration of the home energy consumption prediction system according to the present invention will be described with reference to FIG. As shown in FIG. 1, the in-home energy consumption prediction system according to the present invention includes an in-home device group 100, an energy consumption prediction server 200, and a mobile terminal 300 represented by a tablet terminal and a smartphone. These are connected to each other via the network 400.

  Here, the in-home device group 100 includes an energy consumption measuring device 110 such as a distribution board that measures the energy consumption of an air conditioner, a refrigerator, and the like, and an environmental information acquisition device such as a temperature and humidity sensor that measures the temperature and humidity in the home. 120.

  In addition, the mobile terminal 300 includes a display device 310 having a liquid crystal display unit that displays information received from an energy consumption prediction server 200, which will be described later, and a location information acquisition device 320 that acquires location information of a user such as GPS. A schedule management device 330 that has a schedule function for the user to input and manages the input schedule information is provided. The configuration of the energy consumption prediction server 200 will be described in detail for each of the following embodiments.

<First Embodiment>
A first embodiment of the present invention will be described with reference to FIGS. In this embodiment, the calendar is used to switch between the holiday database and the weekday database stored in the database, and the energy consumption prediction server 200 uses the information received from the in-home device group 100 for energy consumption. The amount is predicted.

<Configuration of energy consumption prediction server>
As shown in FIG. 2, the energy consumption prediction server 200 according to the present embodiment includes a calendar management unit 211, a database 212, a DB (database) type selection unit 213, a predicted value calculation unit 214, and a discomfort index calculation. Part 215. The discomfort index calculation unit 215 is not an essential component and is used when it is desired to further improve the accuracy of the predicted value.

  The calendar management unit 211 has a timekeeping function, and manages whether the day is a holiday or a weekday. The database 212 stores and manages a holiday type database and a weekday type database in which environmental information such as temperature and humidity is associated with energy consumption based on past data. Note that the above database can be further subdivided by registering the family structure.

  The DB (database) type selection unit 213 selects either a holiday database or a weekday database based on the information from the calendar management unit 211 and outputs the selected database to the predicted value calculation unit 214. FIG. 2 shows an example of a weekday database. In the figure, “Tei” indicates the indoor temperature, and “Huj” indicates the indoor humidity. In addition, the database stores power consumption amounts (X11w, X12w, X13w,..., X1nw) for each time zone (T1, T2, T3,..., Tn) on a certain past day. The same applies to the holiday database shown in FIG. 5 and 6 show databases using the discomfort index Dli as a parameter. Here, the database shown in FIG. 6 stores information for each discomfort index DIi in consideration of the correlation with the temperature, temperature, power consumption, and date / time.

  The predicted value calculation unit 214 calculates the predicted energy consumption value for the day based on the environment information and energy consumption measurement information received from the in-home device group 100 and the database output from the DB (database) type selection unit 213. To do. In the present embodiment, the accuracy of the predicted value can be further increased using the discomfort index calculated by the discomfort index calculation unit 215 described later.

Specifically, the calculation of the predicted value in the predicted value calculation unit 214 is, for example, when the actual power consumption at a certain date and time (t _i , d _j ) is X (t _i , d _j ): Is calculated by calculating. Note that, _{t i} is an index indicating each time slot, when dividing one day into time periods of n _is, t 1, · · _·, t i, is defined by · · · _{t n} The D _j is an index representing the day. Note that how many days of data are used can be arbitrarily determined.

Based on the above definition, the following arithmetic expression is calculated to calculate the predicted value.
S (t _i , d _j ) = k * X (t _i , d _j-1 ) + (1-k) * S (t _i , d _j-1 )
That is, using the data in the database, statistical processing (for example, exponential smoothing average (ESMA)) is performed using the above-described arithmetic expression, and the power consumption amount for the day is predicted. Note that k is an exponential smoothing coefficient (0 <k <1), and can be arbitrarily adjusted.

The discomfort index calculation unit 215 calculates the discomfort index (DI) in the following mathematical formula.
(DI) = 0.81Td + 0.01H (0.99Td-14.3) +46.3
Here, Td indicates the dry bulb temperature, and H indicates the humidity. In practice, the above value is not used as it is, but the difference value of the discomfort index based on an arbitrary discomfort index value is used as an index.

  Here, the reason why the discomfort index is used is that there is a close relationship between the discomfort index and the body temperature as shown in FIG. For example, when the temperature is 30 ° C. and the humidity is 60%, the discomfort index (DI) is 80 (hot and sweaty). In general, it is said that 10% of the population becomes uncomfortable when the discomfort index exceeds 75, and everyone becomes uncomfortable when it exceeds 80. In addition, in the case of Japanese, it is said that some people feel uncomfortable when the discomfort index is 77, and 93% are said to feel discomfort due to heat when it reaches 85. Therefore, in the present embodiment, the predicted value can be calculated with high accuracy by calculating the discomfort index and using the discomfort index for the predicted value calculated by the predicted value calculation unit 214 as described above. . Here, when the specific method is mentioned, when extracting the past power consumption value in the predicted time zone from the database shown in FIG. 6, the past data is simply taken out from the same past time zone. Instead, it is possible to extract the power consumption having the difference value of the discomfort index close to the discomfort index calculated by the discomfort index calculation unit 215 and perform the calculation of the predicted value calculation unit 214 using these values. The above description is merely an example, and besides this, a correction method using a discomfort index that can be predicted by those skilled in the art is included.

<Processing of energy consumption prediction server>
The process of the energy consumption prediction server according to this embodiment will be described with reference to FIG.

  First, the energy consumption measuring device 110 measures the energy consumption, transmits the consumption to the energy consumption prediction server 200 (step S101), and uses the environment information acquired by the environment information acquisition device 120 as the energy consumption prediction. It transmits to the server 200 (step S102).

  Next, the energy consumption prediction server 200 selects the database 212 based on the calendar information (step S103), the information received by the energy consumption prediction server 200 and the information in the selected database 212, for example, from the Japan Meteorological Agency. A consumption amount is predicted based on weather forecast data or the like (step S104). Further, if necessary, the discomfort index can be used to calculate the final predicted value (step S105).

  Then, the calculated predicted value or the predicted value using the discomfort index is transmitted to the display device 310 (step S106), and the display device 310 displays the received predicted value (step S107).

  As described above, according to the present embodiment, it is assumed that the lifestyle is greatly different on weekdays and holidays (holidays, weekends), so the database is divided into two types, “weekday type” and “holiday type”. By selecting which database by sorting and calendar, it is possible to predict energy consumption from the current day or the next day with higher accuracy. In addition, it is assumed that an air conditioner such as an air conditioner that takes up most of the power consumption is greatly influenced not only by the temperature but also by the humidity. Therefore, in consideration of these factors, it is possible to predict the energy consumption using the discomfort index as an index and improve the accuracy of the predicted value.

<Second Embodiment>
A second embodiment of the present invention will be described with reference to FIGS. 9 and 10. In the present embodiment, the location information is used to switch between the in-home type database and the out-of-home type database stored in the database, and the energy consumption prediction server 200 uses the information received from the in-home device group 100. The energy consumption is predicted.

<Configuration of energy consumption prediction server>
As shown in FIG. 9, the energy consumption prediction server 200 according to the present embodiment includes a position information receiving unit 221, a database 222, a DB (database) type selection unit 223, a predicted value calculation unit 224, and a discomfort index. And a calculation unit 215. The discomfort index calculation unit 215 is not an essential component and is used when it is desired to further improve the accuracy of the predicted value. In addition, since components having the same reference numerals as those in the first embodiment have the same functions, detailed description thereof is omitted.

  The location information receiving unit 221 receives the user location information from the location information acquisition device 320 such as the GPS of the mobile terminal 300 and outputs the received information to a DB (database) type selection unit 223. The database 222 stores and manages an in-home type database and an out-of-home type database that associate environmental information such as temperature and humidity with energy consumption based on past data. Note that the above database can be further subdivided by registering the family structure.

  The DB (database) type selection unit 223 determines whether the user is in the house or outside the home based on information from the position information receiving unit 221, and either the in-home type database or the out-of-home type database Is output to the predicted value calculation unit 224.

  The predicted value calculation unit 224 calculates the predicted energy consumption value for the day based on the environment information and energy consumption measurement information received from the in-home device group 100 and the database output from the DB (database) type selection unit 223. To do. In the present embodiment, the accuracy of the predicted value can be further increased using the discomfort index calculated by the discomfort index calculation unit 215 described later.

<Processing of energy consumption prediction server>
The process of the energy consumption prediction server according to the present embodiment will be described using FIG.

  First, the energy consumption measuring device 110 measures the energy consumption, transmits the consumption to the energy consumption prediction server 200 (step S201), and uses the environment information acquired by the environment information acquisition device 120 as the energy consumption prediction. It transmits to the server 200 (step S202).

  Next, the energy consumption prediction server 200 selects the database 222 based on in-home or out-of-home information (step S203), and consumption based on the information received by the energy consumption prediction server 200 and the information in the selected database 222. The amount is predicted (step S204). Further, if necessary, the discomfort index can be used to calculate the final predicted value (step S205).

  Then, the calculated predicted value or the predicted value using the discomfort index is transmitted to the display device 310 (step S206), and the display device 310 displays the received predicted value (step S207).

  As described above, according to the present embodiment, two types of predicted values at home and when going out are prepared and used by grasping the user's home status in cooperation with position information. Thus, the prediction accuracy of the energy consumption can be further improved by predicting the energy consumption after the current day or the next day. In addition, it is assumed that an air conditioner such as an air conditioner that takes up most of the power consumption is greatly influenced not only by the temperature but also by the humidity. Therefore, in consideration of these factors, it is possible to predict the energy consumption using the discomfort index as an index and improve the accuracy of the predicted value.

<Third Embodiment>
A third embodiment of the present invention will be described with reference to FIGS. 11 and 12. In the present embodiment, the schedule information is used to switch between the in-home database and the out-of-home database stored in the database, and the energy consumption prediction server 200 uses the information received from the in-home device group 100. The energy consumption is predicted.

<Configuration of energy consumption prediction server>
As shown in FIG. 11, the energy consumption prediction server 200 according to the present embodiment includes a schedule information management unit 231, a database 222, a DB (database) type selection unit 233, a predicted value calculation unit 234, and a discomfort index. And a calculation unit 215. The discomfort index calculation unit 215 is not an essential component and is used when it is desired to further improve the accuracy of the predicted value. In addition, since components having the same reference numerals as those in the first embodiment or the second embodiment have similar functions, detailed description thereof is omitted.

  The schedule information management unit 231 stores and manages schedule information input by the user, and outputs the schedule information to the DB (database) type selection unit 233.

  The DB (database) type selection unit 223 determines whether the user is in the house or outside the house based on the schedule information, selects either the in-house type database or the out-of-home database, and the predicted value The result is output to the calculation unit 234.

  The predicted value calculation unit 234 calculates the predicted energy consumption value for the day based on the environment information and energy consumption measurement information received from the in-home device group 100 and the database output from the DB (database) type selection unit 223. To do. In the present embodiment, the accuracy of the predicted value can be further increased using the discomfort index calculated by the discomfort index calculation unit 215 described later.

<Processing of energy consumption prediction server>
The process of the energy consumption prediction server according to the present embodiment will be described using FIG.

  First, the energy consumption measuring device 110 measures the energy consumption, transmits the consumption to the energy consumption prediction server 200 (step S301), and uses the environment information acquired by the environment information acquisition device 120 as the energy consumption prediction. It transmits to the server 200 (step S302).

  Next, the energy consumption prediction server 200 selects the database 232 based on the in-home or out-of-home information (step S303), and the consumption based on the information received by the energy consumption prediction server 200 and the information in the selected database 232 is used. The amount is predicted (step S304). Further, if necessary, the discomfort index can be used to calculate the final predicted value (step S305).

  Then, the calculated predicted value or the predicted value using the discomfort index is transmitted to the display device 310 (step S306), and the display device 310 displays the received predicted value (step S307).

  As described above, according to the present embodiment, by coordinating with schedule information, it is possible to perform prediction calculation that distinguishes whether the user is in the house or outside the house, and further, energy consumption The amount prediction accuracy can be improved. In addition, it is assumed that an air conditioner such as an air conditioner that takes up most of the power consumption is greatly influenced not only by the temperature but also by the humidity. Therefore, in consideration of these factors, it is possible to predict the energy consumption using the discomfort index as an index and improve the accuracy of the predicted value.

<Modification 1>
In the above-described embodiment, an example of performing the energy consumption prediction on the current day has been described. For example, by using weather forecast data of the Japan Meteorological Agency or the like, it is possible to predict the energy consumption from the next day. In addition, by using past weather data managed by the Japan Meteorological Agency or the like, for example, energy consumption on a monthly basis can be predicted. In this case, in order to predict the energy consumption, for example, weather forecast data of the Japan Meteorological Agency is required.

<Modification 2>
In addition, the first to third embodiments described above can be applied in combination. For example, it is also possible to divide data according to weekdays / holidays, in-home / out-of-home, discomfort index, and to predict energy consumption according to the calendar information of the day, user's schedule, or discomfort index.

  The processing of the home energy consumption prediction system or home energy consumption prediction server is recorded on a computer-readable recording medium, and the program recorded on the recording medium is stored in the home energy consumption prediction system or home energy consumption prediction server. The home energy consumption prediction system or the home energy consumption prediction server of the present invention can be realized by reading and executing. The computer system here includes an OS and hardware such as peripheral devices.

  Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW (World Wide Web) system is used. The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.

  The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

  The embodiments of the present invention have been described in detail with reference to the drawings. However, the specific configuration is not limited to the embodiments, and includes designs and the like that do not depart from the gist of the present invention.

100; In-home device group 110; Energy consumption measuring device 120; Environmental information acquisition device 200; Energy consumption prediction server 211; Calendar management unit 212; Database 213; DB (database) type selection unit 214; Predicted value calculation unit 215; Discomfort index calculation unit 221; position information reception unit 222; database 223; DB (database) type selection unit 224; prediction value calculation unit 231; schedule position information management unit 233; DB (database) type selection unit 234; 300; mobile terminal 310; display device 320; position information acquisition device 330; schedule management device 400; network

Claims (16)

An energy consumption prediction server for predicting energy consumption in the user's home, an energy consumption measurement device connected to the energy consumption prediction server via a network, an environment information acquisition device for acquiring environmental information outside the home, Comprising at least a display device for displaying a prediction result of the energy consumption prediction server,
The energy consumption prediction server is
A database for associating and storing past energy consumption information received from the energy consumption measuring device and the environment information collecting device;
Energy consumption prediction means for predicting energy consumption in the home from information received from the environmental information collection device and information in the database;
With
The home energy consumption prediction system, wherein the display device displays a prediction result of the energy consumption prediction server. The database includes at least a weekday database storing weekday data and a holiday database storing holiday data,
The energy consumption prediction server is
2. The residential energy consumption prediction system according to claim 1, further comprising a calendar function and selection means for selecting a type of the database used for energy consumption prediction based on the calendar information. While comprising at least a position information acquisition device for acquiring the position information of the user,
The database includes a home-type database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home,
The energy consumption prediction server is
Based on the position information acquired by the position information acquisition device, the data processing apparatus includes a selection unit that selects a type of the database depending on whether the user is in the house or outside the house. The home energy consumption prediction system according to claim 1.   The in-home energy consumption prediction system according to claim 3, wherein the position information acquisition device is a GPS in a mobile terminal. A schedule information distribution management device that manages at least the schedule information of the user and transmits the schedule information to the energy consumption prediction server;
The database includes a home-type database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home,
The home energy consumption according to claim 1, further comprising selection means for selecting a type of the database according to the received schedule information when the energy consumption prediction server receives the schedule information. Quantity prediction system.   6. The home database is subdivided into a database storing data corresponding to a combination of the housemates in the user house among the housemates in the user house. Home energy consumption forecasting system described in 1.   The residential energy consumption prediction system according to claim 1, wherein the energy consumption prediction server acquires an unpleasantness index. Connected to the energy consumption measuring device that measures the energy consumption in the home, the environment information acquisition device that obtains the environmental information outside the home, and the display device via the network, and displays at least the energy consumption prediction result in the home A home energy consumption prediction server,
A database for associating and storing past energy consumption information received from the energy consumption measuring device and the environment information collecting device;
Energy consumption prediction means for predicting energy consumption in the home from information received from the environmental information collection device and information in the database;
A home energy consumption forecasting server characterized by comprising: The database includes at least a weekday database storing weekday data and a holiday database storing holiday data,
9. The residential energy consumption prediction server according to claim 8, further comprising a calendar function and selection means for selecting a type of the database used for energy consumption prediction based on the calendar information. The database includes an in-home database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home,
Selection for selecting the type of the database based on at least the position information acquired by the position information acquisition device that acquires the position information of the user, depending on whether the user is in the house or outside the house The in-home energy consumption prediction server according to claim 8, further comprising means.   The in-home energy consumption prediction server according to claim 10, wherein the position information acquisition device is a GPS in a mobile terminal. A schedule information distribution management device that manages at least the schedule information of the user and transmits the schedule information to the energy consumption prediction server;
The database includes a home-type database that stores data when the user is at home and an out-of-home database that stores data when the user is outside the home,
9. The residential energy consumption prediction server according to claim 8, further comprising selection means for selecting the type of the database according to the received schedule information when the schedule information is received.   13. The home database is subdivided into a database storing data corresponding to a combination of the housemates in the user house among the housemates in the user house. Home energy consumption prediction server described in 1.   The residential energy consumption prediction system according to claim 8, wherein a discomfort index is acquired. An energy consumption prediction server for predicting energy consumption in the user's home, an energy consumption measurement device connected to the energy consumption prediction server via a network, an environment information acquisition device for acquiring environmental information outside the home, A home energy consumption prediction method in a home energy consumption prediction system comprising at least a display device for displaying a prediction result of the energy consumption prediction server,
The energy consumption prediction server has a database for storing past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other,
A home energy consumption prediction method in which the energy consumption prediction server predicts energy consumption in a home from information received from the environmental information collection device and information in the database. An energy consumption prediction server for predicting energy consumption in the user's home, an energy consumption measurement device connected to the energy consumption prediction server via a network, an environment information acquisition device for acquiring environmental information outside the home, A program for causing a computer to execute a home energy consumption prediction method in a home energy consumption prediction system including at least a display device that displays a prediction result of the energy consumption prediction server,
The energy consumption prediction server has a database for storing past energy consumption information received from the energy consumption measurement device and the environment information collection device in association with each other,
A program in which the energy consumption prediction server predicts energy consumption in a home from information received from the environmental information collection device and information in the database.

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